# Financial Relation Extraction
## Process
Detecting the presence of a relationship between financial terms and qualifying the relationship in case of its presence. Example use cases:
* An A-B trust is a joint trust created by a married couple for the purpose of minimizing estate taxes. (Relationship **exists**, type: **is**)
* There are no withdrawal penalties. (Relationship **does not exist**, type: **x**)
## Data
The data consists of financial definitions collected from different sources (Wikimedia, IFRS, Investopedia) for financial indicators. Each definition has been split up into sentences, and term relationships in a sentence have been extracted using the [Stanford Open Information Extraction](https://nlp.stanford.edu/software/openie.html) module.
A typical row in the dataset consists of a definition sentence and its corresponding relationship label.
The labels were restricted to the 5 most-widely identified relationships, namely: **x** (no relationship), **has**, **is in**, **is** and **are**.
## Model
The model used is a standard Roberta-base transformer model from the Hugging Face library. See [HUGGING FACE DistilBERT base model](https://huggingface.co./distilbert-base-uncased) for more details about the model.
In addition, the model has been pretrained to initializa weigths that would otherwise be unused if loaded from an existing pretrained stock model.
## Metrics
The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
| relation | precision | recall | f1-score | support |
| ------------- |:-------------:|:-------------:|:-------------:| -----:|
| has | 0.7416 | 0.9674 | 0.8396 | 2362 |
| is in | 0.7813 | 0.7925 | 0.7869 | 2362 |
| is | 0.8650 | 0.6863 | 0.7653 | 2362 |
| are | 0.8365 | 0.8493 | 0.8429 | 2362 |
| x | 0.9515 | 0.8302 | 0.8867 | 2362 |
| | | | | |
| macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
| weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 |